Do I need git when using AI coding assistants?

Yes, git is still essential for collaboration, code review, and deployment. But you also need automatic snapshots for protection during AI iteration, since git alone leaves gaps.

No. AI coding assistants generate code, but you still need version control for history, collaboration, and deployment. What's changing is that you need additional protection during AI iteration.

What version control do I need for AI coding?

You need git for intentional history (commits, branches, collaboration) plus automatic snapshots for continuous protection during rapid AI iteration. They solve different problems.

Is git enough for Cursor and Claude Code?

Git is necessary but not sufficient. AI iteration creates rapid changes between commits. Use git for milestones and automatic snapshots (like mrq) for continuous protection.

Do You Still Need Git When Using AI Coding Assistants?

AI coding assistants are changing how developers work. Cursor, Claude Code, and Copilot can generate entire features, refactor codebases, and iterate faster than any human.

So do you still need git? Is version control even relevant when AI is doing most of the work?

The short answer: yes, you still need git. But you also need something else.

What Git Is Still For

Git isn’t going anywhere. You still need it for:

Collaboration: Sharing code with teammates, code review, pull requests. AI doesn’t change the fact that multiple developers work on the same codebase.

Deployment: CI/CD pipelines, production releases, rollbacks. Your deployment infrastructure is built around git.

History: Understanding how your project evolved, when features were added, why decisions were made. Meaningful commits tell a story.

Branching: Experimenting with features, managing releases, isolating work. The git branching model is still valuable.

AI coding assistants generate code. Git manages how that code moves through your development process.

What Git Doesn’t Handle

Here’s where things get complicated.

Git was designed for deliberate, discrete changes. You decide to make a change, work on it, commit it. There’s a natural rhythm.

AI coding breaks this rhythm:

Changes happen in rapid bursts
You try multiple approaches quickly
The AI might modify files you didn’t ask about
You’re focused on reviewing, not committing

During AI iteration, you’re often in a state where:

You haven’t committed in 15 minutes
The AI has changed 20 files
Some changes work, some don’t
You’re not sure which state you want to keep

If something breaks right now, git can’t help. It only protects what you committed.

The Gap

Git has always had this gap of unprotected time between commits. But with traditional coding, that gap was small. You’d make a few changes, commit, continue.

With AI coding, the gap is huge. AI moves fast, and you don’t want to interrupt the flow with commits. So you end up with long stretches of unprotected iteration.

And those stretches are exactly when AI is most likely to break something.

What You Actually Need

You need two layers:

Layer 1: Intentional History (Git)

Clean commits at meaningful milestones
Collaboration, code review, deployment
The story of how your project evolved

Layer 2: Continuous Protection (Automatic Snapshots)

Every change captured automatically
No decisions, no friction
Every state recoverable, not just commits

mrq provides the second layer:

mrq watch

Now every change is captured. When AI breaks something:

mrq restore abc123

Back to any point, including states you never would have committed.

The Combined Workflow

Here’s what version control looks like for AI coding:

# Start your session
mrq watch                    # Continuous protection

# Work with AI
# Iterate freely, try things, explore
# Everything captured automatically

# Hit a milestone
git add -A
git commit -m "Search feature complete"   # Clean history

# Push for review
git push origin feature-branch

# Collaborate normally
# PR, review, merge as usual

# Something broke during iteration?
mrq restore abc123           # Recover any state

Git handles what git is for: meaningful history, collaboration, deployment.

mrq handles what’s missing: continuous protection during iteration.

Answering the Question

Do you still need git? Yes. For collaboration, deployment, history, and code review.

Is git enough? No. AI coding creates gaps where you’re unprotected.

What else do you need? Automatic snapshots that capture every change without requiring commits.

Git is essential. It’s just not sufficient anymore.

Summary

Git is still the backbone of professional development. AI doesn’t change that.

What’s changing is that AI iteration creates long gaps between commits, gaps where you’re unprotected and where AI is most likely to break things.

The solution is adding automatic snapshots to your workflow:

Git for intentional history and collaboration
Automatic snapshots for continuous protection

You still need git. You just need more than git.

mrq vs Git for AI Coding - Direct comparison
Git for AI Coding: What’s Missing - The gap explained
Why Git Commits Don’t Work for AI Iteration - The friction problem
Git Alternatives for AI Coding - What else exists

mrq adds continuous protection to your git workflow. Every change captured, instant recovery when AI breaks things.